code: def block(
      X,
      block_name,
      use_fp16,
      pool_idx=None,
      encoder_state=None,
      train=False,
      pdrop=0.1,
      nominal_pool_length=512,
      use_fused_kernel=True,
  ):
      with tf.compat.v1.variable_scope(block_name):
          h1 = cumulative_state_net(
              X,
              "cumulative_state_net",
              use_fp16,
              pdrop,
              train,
              nominal_pool_length=nominal_pool_length,
              use_fused_kernel=use_fused_kernel,
          )
          if encoder_state is not None:
              mixed = enc_dec_mix(
                  encoder_state["sequence_features"],
                  h1,
                  encoder_state["pool_idx"],
                  pool_idx,
              )
              h1 = h1 + mixed
          output = tf.nn.relu(
              dropout(
                  norm(h1 + X, "norm", fp16=use_fp16, e=1e-2),
                  pdrop,
                  train,
              )
          )
          return output
  
file_path: finetune/finetune/base_models/oscar/featurizer.py
imports: import tensorflow
  from finetune.base_models.gpt.featurizer import dropout, embed, split_heads, merge_heads
  from finetune.util.shapes import shape_list, lengths_from_eos_idx
  from finetune.custom_ops import recursive_agg
  from finetune.base_models.gpt.featurizer import get_pos_values
  from finetune.optimizers.recompute_grads import recompute_grad
  import functools
label_desc: Use image node_id 10 for calling a global function with example usage: block(X, block_name, use_fp16, pool_idx, encoder_state, train, pdrop, nominal_pool_length, use_fused_kernel) and returns: output